yat
0.8.3pre
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Polynomial regression. More...
#include <yat/regression/Polynomial.h>
Public Member Functions | |
Polynomial (size_t power) | |
~Polynomial (void) | |
Destructor. | |
const utility::Matrix & | covariance (void) const |
covariance of parameters | |
void | fit (const utility::VectorBase &x, const utility::VectorBase &y) |
const utility::Vector & | fit_parameters (void) const |
double | predict (const double x) const |
double | s2 (void) const |
double | standard_error2 (const double x) const |
double | chisq (void) const |
Chi-squared. | |
double | prediction_error2 (const double x) const |
std::ostream & | print (std::ostream &os, const double min, double max, const unsigned int n) const |
print output to ostream os | |
double | r2 (void) const |
Protected Member Functions | |
double | variance (void) const |
Protected Attributes | |
statistics::AveragerPair | ap_ |
double | chisq_ |
Polynomial regression.
Data are modeled as
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explicit |
power | degree of polynomial, e.g. 1 for a linear model |
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inherited |
Chi-squared.
Chi-squared is defined as the
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virtual |
Fit the model by minimizing the mean squared deviation between model and data.
Implements theplu::yat::regression::OneDimensional.
const utility::Vector& theplu::yat::regression::Polynomial::fit_parameters | ( | void | ) | const |
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virtual |
Implements theplu::yat::regression::OneDimensional.
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inherited |
The prediction error is defined as the expected squared deviation a new data point will have from value the model provides: and is typically divided into the conditional variance ( see s2() ) given and the squared standard error ( see standard_error2() ) of the model estimation in .
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inherited |
print output to ostream os
Printing estimated model to os in the points defined by min, max, and n. The values printed for each point is the x-value, the estimated y-value, and the estimated standard deviation of a new data poiunt will have from the y-value given the x-value (see prediction_error()).
os | Ostream printout is sent to |
n | number of points printed |
min | smallest x-value for which the model is printed |
max | largest x-value for which the model is printed |
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inherited |
r2 is defined as or the fraction of the variance explained by the regression model.
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virtual |
where DF is number of parameters in model.
Implements theplu::yat::regression::OneDimensional.
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virtual |
Implements theplu::yat::regression::OneDimensional.
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protectedinherited |
Variance of y
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protectedinherited |
Averager for pair of x and y
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protectedinherited |